Resource allocation in online systems (e.g. data centers, server pools) can be driven by performance predictions, such as estimates of future incoming loads to servers and/or of the quality-of-service (QoS) offered by applications to end users. In this context, accurately characterizing web workload fluctuations supports the provision of resources under time-varying traffic intensities. Fitting and predicting web workloads, for example, supports system management tasks, such as deployment and provisioning, as well as the design of cloud computing solutions, selecting load balancing and scheduling policies, and performing capacity planning exercises. As data centers become larger and their workloads increase in complexity, performing such activities via trial-and-error can become impractical.